1398 lines
No EOL
60 KiB
Python
1398 lines
No EOL
60 KiB
Python
"""
|
|
CoderAgent - A unified agent for developing and executing Python code.
|
|
Includes code execution capabilities previously in separate modules.
|
|
Enhanced with auto-correction loop for handling execution errors.
|
|
"""
|
|
|
|
import logging
|
|
import json
|
|
import re
|
|
import uuid
|
|
import traceback
|
|
import os
|
|
import subprocess
|
|
import tempfile
|
|
import shutil
|
|
import sys
|
|
import pandas as pd
|
|
from datetime import datetime
|
|
from typing import List, Dict, Any, Optional, Tuple
|
|
|
|
from modules.agentservice_base import BaseAgent
|
|
from modules.agentservice_utils import FileUtils, WorkflowUtils, MessageUtils, LoggingUtils
|
|
from connectors.connector_aichat_openai import ChatService
|
|
from modules.agentservice_protocol import AgentMessage, AgentCommunicationProtocol
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
# Existing SimpleCodeExecutor class remains unchanged
|
|
class SimpleCodeExecutor:
|
|
# ... existing code ...
|
|
"""
|
|
A simplified executor that runs Python code in isolated virtual environments.
|
|
"""
|
|
|
|
# Class variable to store workflow environments for persistence
|
|
_workflow_environments = {}
|
|
|
|
def __init__(self,
|
|
workflow_id: str = None,
|
|
timeout: int = 30,
|
|
max_memory_mb: int = 512,
|
|
requirements: List[str] = None,
|
|
blocked_packages: List[str] = None,
|
|
ai_service = None):
|
|
"""
|
|
Initialize the SimpleCodeExecutor.
|
|
|
|
Args:
|
|
workflow_id: Optional workflow ID for persistent environments
|
|
timeout: Maximum execution time in seconds
|
|
max_memory_mb: Maximum memory in MB
|
|
requirements: List of packages to install
|
|
blocked_packages: List of blocked packages
|
|
"""
|
|
self.workflow_id = workflow_id
|
|
self.timeout = timeout
|
|
self.max_memory_mb = max_memory_mb
|
|
self.temp_dir = None
|
|
self.requirements = requirements or []
|
|
self.blocked_packages = blocked_packages or [
|
|
"cryptography", "flask", "django", "tornado", # Security risks
|
|
"tensorflow", "pytorch", "scikit-learn" # Resource intensive
|
|
]
|
|
self.is_persistent = workflow_id is not None
|
|
self.ai_service = ai_service
|
|
|
|
@classmethod
|
|
def get_workflow_environment(cls, workflow_id: str) -> Optional[str]:
|
|
"""Get an existing workflow environment path if it exists."""
|
|
return cls._workflow_environments.get(workflow_id)
|
|
|
|
@classmethod
|
|
def set_workflow_environment(cls, workflow_id: str, env_path: str) -> None:
|
|
"""Store a workflow environment path."""
|
|
cls._workflow_environments[workflow_id] = env_path
|
|
|
|
def _create_venv(self) -> str:
|
|
"""Creates a virtual environment and returns the path."""
|
|
# Check for existing environment if using workflow_id
|
|
if self.workflow_id:
|
|
self.is_persistent = True
|
|
existing_env = self.get_workflow_environment(self.workflow_id)
|
|
if existing_env and os.path.exists(existing_env):
|
|
logger.info(f"Reusing existing virtual environment: {existing_env}")
|
|
self.temp_dir = os.path.dirname(existing_env)
|
|
return existing_env
|
|
else:
|
|
logger.info(f"Creating new environment for workflow {self.workflow_id}")
|
|
|
|
# Create a new environment
|
|
venv_parent_dir = tempfile.mkdtemp(prefix="simple_exec_")
|
|
self.temp_dir = venv_parent_dir
|
|
venv_path = os.path.join(venv_parent_dir, "venv")
|
|
|
|
try:
|
|
# Create virtual environment
|
|
logger.info(f"Creating new virtual environment in {venv_path}")
|
|
subprocess.run([sys.executable, "-m", "venv", venv_path],
|
|
check=True,
|
|
capture_output=True)
|
|
|
|
# Store the environment path if this is for a specific workflow
|
|
if self.workflow_id:
|
|
logger.info(f"Registering new persistent environment for workflow {self.workflow_id}")
|
|
self.set_workflow_environment(self.workflow_id, venv_path)
|
|
|
|
return venv_path
|
|
except subprocess.CalledProcessError as e:
|
|
logger.error(f"Error creating virtual environment: {e}")
|
|
raise RuntimeError(f"Could not create venv: {e}")
|
|
|
|
def _get_pip_executable(self, venv_path: str) -> str:
|
|
"""Gets the path to the pip executable in the virtual environment."""
|
|
if os.name == 'nt': # Windows
|
|
return os.path.join(venv_path, "Scripts", "pip.exe")
|
|
else: # Unix/Linux
|
|
return os.path.join(venv_path, "bin", "pip")
|
|
|
|
def _get_python_executable(self, venv_path: str) -> str:
|
|
"""Gets the path to the Python executable in the virtual environment."""
|
|
if os.name == 'nt': # Windows
|
|
return os.path.join(venv_path, "Scripts", "python.exe")
|
|
else: # Unix/Linux
|
|
return os.path.join(venv_path, "bin", "python")
|
|
|
|
def _filter_requirements(self, requirements: List[str]) -> List[str]:
|
|
"""Filter out blocked packages and invalid requirements."""
|
|
if not requirements:
|
|
return []
|
|
|
|
filtered_requirements = []
|
|
for req in requirements:
|
|
# Skip empty, comment lines, or invalid requirements
|
|
req = req.strip()
|
|
if not req or req.startswith('#') or '```' in req or req in ['`', '``', '```']:
|
|
logging.warning(f"Skipping comment or invalid requirement: {req}")
|
|
continue
|
|
|
|
# Extract package name from requirement spec
|
|
import re
|
|
package_name = re.split(r'[=<>]', req)[0].strip().lower()
|
|
|
|
if package_name in self.blocked_packages:
|
|
logging.warning(f"Blocked package detected: {package_name}")
|
|
continue
|
|
|
|
filtered_requirements.append(req)
|
|
|
|
return filtered_requirements
|
|
|
|
def _install_packages(self, venv_path: str, requirements: List[str]) -> bool:
|
|
"""Install packages in the virtual environment."""
|
|
if not requirements:
|
|
return True
|
|
|
|
# Filter requirements
|
|
filtered_requirements = self._filter_requirements(requirements)
|
|
if not filtered_requirements:
|
|
logger.info("No allowed packages to install")
|
|
return True
|
|
|
|
# Get pip executable
|
|
pip_executable = self._get_pip_executable(venv_path)
|
|
|
|
# Install packages
|
|
try:
|
|
logger.info(f"Installing packages: {', '.join(filtered_requirements)}")
|
|
result = subprocess.run(
|
|
[pip_executable, "install"] + filtered_requirements,
|
|
check=True,
|
|
capture_output=True,
|
|
text=True,
|
|
timeout=300
|
|
)
|
|
logger.info("Package installation successful")
|
|
return True
|
|
except subprocess.CalledProcessError as e:
|
|
logger.error(f"Error during package installation: {e.stderr}")
|
|
return False
|
|
except Exception as e:
|
|
logger.error(f"Error during package installation: {str(e)}")
|
|
return False
|
|
|
|
def _extract_required_packages(self, code: str) -> List[str]:
|
|
# Extract required packages from requirements comments in the 1st code line
|
|
packages = set()
|
|
# Check for special REQUIREMENTS comment - specific format we're looking for
|
|
first_lines = code.split('\n')[:5] # Only check first few lines
|
|
for line in first_lines:
|
|
if line.strip().startswith("# REQUIREMENTS:"):
|
|
req_str = line.replace("# REQUIREMENTS:", "").strip()
|
|
for pkg in req_str.split(','):
|
|
if pkg.strip():
|
|
packages.add(pkg.strip())
|
|
return list(packages)
|
|
|
|
|
|
def execute_code(self, code: str, input_data: Dict[str, Any] = None) -> Dict[str, Any]:
|
|
"""
|
|
Execute Python code in an isolated environment using a simple approach.
|
|
|
|
Args:
|
|
code: Python code to execute
|
|
input_data: Optional input data for the code
|
|
|
|
Returns:
|
|
Dictionary with execution results
|
|
"""
|
|
logger.info(f"Executing code with workflow_id: {self.workflow_id}")
|
|
|
|
# Create or reuse virtual environment
|
|
venv_path = self._create_venv() #creating self.temp_dir!
|
|
|
|
# Create input_data directory for file handling
|
|
input_data_dir = os.path.join(self.temp_dir, "input_data") # Temp dir is at root
|
|
os.makedirs(input_data_dir, exist_ok=True)
|
|
|
|
# Extract and install required packages
|
|
all_requirements = []
|
|
|
|
# Add explicitly provided requirements
|
|
# if self.requirements:
|
|
# all_requirements.extend(self.requirements)
|
|
|
|
# Extract requirements from code
|
|
extracted_requirements = self._extract_required_packages(code)
|
|
if extracted_requirements:
|
|
all_requirements.extend(extracted_requirements)
|
|
logger.info(f"Extracted required packages from code: {', '.join(extracted_requirements)}")
|
|
|
|
# Install packages if needed
|
|
if all_requirements:
|
|
logger.info(f"Installing {len(all_requirements)} packages")
|
|
install_success = self._install_packages(venv_path, all_requirements)
|
|
if not install_success:
|
|
# Return error if package installation failed
|
|
return {
|
|
"success": False,
|
|
"output": "",
|
|
"error": f"Failed to install required packages: {', '.join(all_requirements)}",
|
|
"result": None,
|
|
"exit_code": -1
|
|
}
|
|
|
|
# Process extracted document content if available
|
|
if input_data and "extracted_documents" in input_data:
|
|
for doc in input_data["extracted_documents"]:
|
|
doc_name = doc["name"]
|
|
doc_content = doc["content"]
|
|
doc_type = doc["type"]
|
|
|
|
# Create file path
|
|
file_path = os.path.join(input_data_dir, doc_name)
|
|
|
|
try:
|
|
# Write content to file
|
|
with open(file_path, 'w', encoding='utf-8') as f:
|
|
f.write(doc_content)
|
|
|
|
# Add to files list if not already there
|
|
if "files" not in input_data:
|
|
input_data["files"] = []
|
|
|
|
input_data["files"].append({
|
|
"id": f"extracted_{doc_name}",
|
|
"name": doc_name,
|
|
"type": doc_type,
|
|
"path": file_path
|
|
})
|
|
|
|
logger.info(f"Created file from extracted content: {doc_name}")
|
|
except Exception as e:
|
|
logger.error(f"Error creating file from extracted content: {str(e)}")
|
|
|
|
# Copy input files to input_data directory if provided
|
|
if input_data and "files" in input_data:
|
|
for file_info in input_data.get("files", []):
|
|
# Skip files we just created from extracted content
|
|
if file_info.get("id", "").startswith("extracted_"):
|
|
continue
|
|
|
|
source_path = file_info.get("path", "")
|
|
logger.info(f"Attempting to copy file from: {source_path}")
|
|
logger.info(f"File exists: {os.path.exists(source_path)}")
|
|
if source_path and os.path.exists(source_path):
|
|
# Get just the filename
|
|
file_name = os.path.basename(source_path)
|
|
# Create destination path in input_data directory
|
|
dest_path = os.path.join(input_data_dir, file_name)
|
|
|
|
try:
|
|
# Copy the file
|
|
shutil.copy2(source_path, dest_path)
|
|
logger.info(f"Copied file to input_data directory: {dest_path}")
|
|
except Exception as e:
|
|
logger.error(f"Error copying file {source_path}: {str(e)}")
|
|
|
|
# Create a file for the code
|
|
code_id = uuid.uuid4().hex[:8]
|
|
code_file = os.path.join(self.temp_dir, f"code_{code_id}.py")
|
|
|
|
# Write the code as-is without injecting additional loader code
|
|
with open(code_file, "w", encoding="utf-8") as f:
|
|
f.write(code)
|
|
|
|
# Get Python executable
|
|
python_executable = self._get_python_executable(venv_path)
|
|
logger.info(f"Using Python executable: {python_executable}")
|
|
|
|
# Execute code
|
|
try:
|
|
# Run the code from root dir
|
|
working_dir = os.path.dirname(code_file) # This should be the project root
|
|
logger.info(f"DEBUG PATH Root: {os.getcwd()} Code: {code_file} Working Dir: {working_dir}")
|
|
logger.debug(f"|{code}|")
|
|
process = subprocess.run(
|
|
[python_executable, code_file],
|
|
timeout=self.timeout,
|
|
capture_output=True,
|
|
text=True,
|
|
cwd=working_dir
|
|
)
|
|
|
|
# Process the output
|
|
stdout = process.stdout
|
|
stderr = process.stderr
|
|
|
|
# Get result from stdout if available
|
|
result_data = None
|
|
if process.returncode == 0 and stdout:
|
|
try:
|
|
# Look for the last line that could be JSON
|
|
for line in reversed(stdout.strip().split('\n')):
|
|
line = line.strip()
|
|
if line and line[0] in '{[' and line[-1] in '}]':
|
|
try:
|
|
result_data = json.loads(line)
|
|
# Successfully parsed JSON result, use it
|
|
break
|
|
except json.JSONDecodeError:
|
|
# Not valid JSON, continue to next line
|
|
continue
|
|
except Exception as e:
|
|
logger.warning(f"Failed to parse result from stdout: {str(e)}")
|
|
|
|
# Create result dictionary
|
|
execution_result = {
|
|
"success": process.returncode == 0,
|
|
"output": stdout,
|
|
"error": stderr if process.returncode != 0 else "",
|
|
"result": result_data,
|
|
"exit_code": process.returncode
|
|
}
|
|
|
|
except subprocess.TimeoutExpired:
|
|
logger.error(f"Execution timed out after {self.timeout} seconds")
|
|
execution_result = {
|
|
"success": False,
|
|
"output": "",
|
|
"error": f"Execution timed out (timeout after {self.timeout} seconds)",
|
|
"result": None,
|
|
"exit_code": -1
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"Execution error: {str(e)}")
|
|
execution_result = {
|
|
"success": False,
|
|
"output": "",
|
|
"error": f"Execution error: {str(e)} for code {code}",
|
|
"result": None,
|
|
"exit_code": -1
|
|
}
|
|
|
|
# Clean up temporary code file
|
|
try:
|
|
if os.path.exists(code_file):
|
|
os.remove(code_file)
|
|
except Exception as e:
|
|
logger.warning(f"Error cleaning up temporary code file: {e}")
|
|
|
|
return execution_result
|
|
|
|
def cleanup(self):
|
|
"""Clean up temporary resources."""
|
|
# Skip cleanup for persistent environments
|
|
if self.is_persistent and self.workflow_id:
|
|
logger.info(f"Skipping cleanup for persistent environment of workflow {self.workflow_id}")
|
|
return
|
|
|
|
# Clean up temporary directory
|
|
if self.temp_dir and os.path.exists(self.temp_dir):
|
|
try:
|
|
shutil.rmtree(self.temp_dir)
|
|
logger.info(f"Deleted temporary directory: {self.temp_dir}")
|
|
except Exception as e:
|
|
logger.warning(f"Could not delete temporary directory {self.temp_dir}: {e}")
|
|
|
|
def __del__(self):
|
|
"""Clean up during garbage collection."""
|
|
self.cleanup()
|
|
|
|
# Unchanged error recommendation function
|
|
def get_error_recommendation(error_message: str) -> str:
|
|
"""Generate recommendations based on error message."""
|
|
if "ImportError" in error_message or "ModuleNotFoundError" in error_message:
|
|
return """
|
|
### Recommendation
|
|
The error indicates a missing Python module. Try using standard libraries or common data analysis modules.
|
|
"""
|
|
elif "PermissionError" in error_message:
|
|
return """
|
|
### Recommendation
|
|
The code doesn't have the necessary permissions to access files or directories.
|
|
"""
|
|
elif "SyntaxError" in error_message:
|
|
return """
|
|
### Recommendation
|
|
There's a syntax error in the code. Check for missing parentheses, quotes, colons, or indentation errors.
|
|
"""
|
|
elif "FileNotFoundError" in error_message:
|
|
return """
|
|
### Recommendation
|
|
A file could not be found. Check the file path and make sure the file exists.
|
|
"""
|
|
else:
|
|
return """
|
|
### Recommendation
|
|
To fix the error:
|
|
1. Check the exact error message
|
|
2. Simplify the code and test step by step
|
|
3. Use try/except blocks for error-prone operations
|
|
"""
|
|
|
|
|
|
class CoderAgent(BaseAgent):
|
|
"""Agent for developing and executing Python code with auto-correction capabilities"""
|
|
|
|
def __init__(self):
|
|
"""Initialize the coder agent with proper type and capabilities"""
|
|
super().__init__()
|
|
|
|
# Agent metadata
|
|
self.id = "coder"
|
|
self.type = "coder"
|
|
self.name = "Python Code Agent"
|
|
self.description = "Develops and executes Python code"
|
|
self.capabilities = "code_development,data_processing,file_processing,automation"
|
|
self.result_format = "python_code"
|
|
|
|
# Initialize AI service
|
|
self.ai_service = None
|
|
|
|
# Add document capabilities
|
|
self.supports_documents = True
|
|
self.document_capabilities = ["read", "reference", "create"]
|
|
self.required_context = ["workflow_id"]
|
|
self.document_handler = None
|
|
|
|
# Initialize protocol
|
|
self.protocol = AgentCommunicationProtocol()
|
|
|
|
# Init utilities
|
|
self.file_utils = FileUtils()
|
|
self.message_utils = MessageUtils()
|
|
|
|
# Executor settings
|
|
self.executor_timeout = 60 # seconds
|
|
self.executor_memory_limit = 512 # MB
|
|
|
|
# AI service settings
|
|
self.ai_temperature = 0.1 # Lower temperature for more deterministic code generation
|
|
self.ai_max_tokens = 2000 # Enough tokens for complex code
|
|
|
|
# Auto-correction settings (new)
|
|
self.max_correction_attempts = 3 # Maximum number of correction attempts
|
|
self.correction_temperature = 0.1 # Even lower temperature for corrections
|
|
|
|
def get_agent_info(self) -> Dict[str, Any]:
|
|
"""Get agent information for agent registry"""
|
|
info = super().get_agent_info()
|
|
info.update({
|
|
"metadata": {
|
|
"timeout": self.executor_timeout,
|
|
"memory_limit": self.executor_memory_limit,
|
|
"max_correction_attempts": self.max_correction_attempts
|
|
}
|
|
})
|
|
return info
|
|
|
|
def set_document_handler(self, document_handler):
|
|
"""Set the document handler for file operations"""
|
|
self.document_handler = document_handler
|
|
|
|
|
|
async def process_message(self, message: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]:
|
|
"""
|
|
Process a message to develop and execute Python code with auto-correction.
|
|
|
|
Args:
|
|
message: The message to process
|
|
context: Additional context information
|
|
|
|
Returns:
|
|
Response message
|
|
"""
|
|
# Extract workflow_id from context or message
|
|
workflow_id = context.get("workflow_id") if context else message.get("workflow_id", "unknown")
|
|
|
|
# Get or create logging_utils
|
|
log_func = context.get("log_func") if context else None
|
|
logging_utils = LoggingUtils(workflow_id, log_func)
|
|
|
|
# Create response message
|
|
response = {
|
|
"role": "assistant",
|
|
"content": "",
|
|
"agent_id": self.id,
|
|
"agent_type": self.type,
|
|
"agent_name": self.name,
|
|
"workflow_id": workflow_id,
|
|
"documents": []
|
|
}
|
|
|
|
# Send status update using protocol
|
|
if log_func:
|
|
status_message = self.protocol.create_status_update_message(
|
|
status_description="Starting code generation and execution",
|
|
sender_id=self.id,
|
|
status="in_progress",
|
|
progress=0.0,
|
|
context_id=workflow_id
|
|
)
|
|
log_func(workflow_id, status_message.content, "info", self.id, self.name)
|
|
|
|
try:
|
|
# Extract content and documents
|
|
content = message.get("content", "")
|
|
documents = message.get("documents", [])
|
|
|
|
code_to_execute = None
|
|
requirements = []
|
|
|
|
# Generate code based on the message content using AI
|
|
logging_utils.info("Generating new code with AI", "agents")
|
|
|
|
# Log status update - 10% progress
|
|
if log_func:
|
|
status_message = self.protocol.create_status_update_message(
|
|
status_description="Analyzing requirements and generating code",
|
|
sender_id=self.id,
|
|
status="in_progress",
|
|
progress=0.1,
|
|
context_id=workflow_id
|
|
)
|
|
log_func(workflow_id, status_message.content, "info", self.id, self.name)
|
|
|
|
# Generate code using AI
|
|
code_to_execute, requirements = await self._generate_code_from_prompt(content, documents)
|
|
if not code_to_execute:
|
|
logging_utils.warning("AI could not generate code", "agents")
|
|
response["content"] = "I couldn't generate executable code based on your request. Please provide more detailed instructions."
|
|
self.message_utils.finalize_message(response)
|
|
return response
|
|
|
|
logging_utils.info(f"Code generated with AI ({len(code_to_execute)} characters)", "agents")
|
|
|
|
# Log status update - 30% progress
|
|
if log_func:
|
|
status_message = self.protocol.create_status_update_message(
|
|
status_description="Code generated, preparing for execution",
|
|
sender_id=self.id,
|
|
status="in_progress",
|
|
progress=0.3,
|
|
context_id=workflow_id
|
|
)
|
|
log_func(workflow_id, status_message.content, "info", self.id, self.name)
|
|
|
|
# Create code file document
|
|
code_doc_id = f"code_{uuid.uuid4()}"
|
|
code_filename = "generated_code.py"
|
|
|
|
code_document = {
|
|
"id": code_doc_id,
|
|
"source": {
|
|
"type": "generated",
|
|
"id": code_doc_id,
|
|
"name": code_filename,
|
|
"content_type": "text/x-python",
|
|
"size": len(code_to_execute)
|
|
},
|
|
"contents": [{
|
|
"type": "text",
|
|
"text": code_to_execute,
|
|
"is_extracted": True
|
|
}]
|
|
}
|
|
|
|
# Add code document to response
|
|
response["documents"].append(code_document)
|
|
logging_utils.info(f"Added code file '{code_filename}' to response", "agents")
|
|
|
|
# Execute the code with auto-correction loop
|
|
if code_to_execute:
|
|
# Log status update - 40% progress
|
|
if log_func:
|
|
status_message = self.protocol.create_status_update_message(
|
|
status_description="Setting up execution environment",
|
|
sender_id=self.id,
|
|
status="in_progress",
|
|
progress=0.4,
|
|
context_id=workflow_id
|
|
)
|
|
log_func(workflow_id, status_message.content, "info", self.id, self.name)
|
|
|
|
# Prepare execution context
|
|
execution_context = {
|
|
"workflow_id": workflow_id,
|
|
"documents": documents,
|
|
"message": message,
|
|
"log_func": log_func
|
|
}
|
|
|
|
# Log status update - 50% progress
|
|
if log_func:
|
|
status_message = self.protocol.create_status_update_message(
|
|
status_description="Executing code",
|
|
sender_id=self.id,
|
|
status="in_progress",
|
|
progress=0.5,
|
|
context_id=workflow_id
|
|
)
|
|
log_func(workflow_id, status_message.content, "info", self.id, self.name)
|
|
|
|
# Enhanced execution with auto-correction
|
|
result, attempts_info = await self._execute_with_auto_correction(
|
|
code_to_execute,
|
|
requirements,
|
|
execution_context,
|
|
content, # Original prompt/message
|
|
logging_utils
|
|
)
|
|
|
|
# Prepare response based on the final result (success or failure)
|
|
if result.get("success", False):
|
|
# Log status update - 80% progress
|
|
if log_func:
|
|
status_message = self.protocol.create_status_update_message(
|
|
status_description="Code executed successfully, preparing results",
|
|
sender_id=self.id,
|
|
status="in_progress",
|
|
progress=0.8,
|
|
context_id=workflow_id
|
|
)
|
|
log_func(workflow_id, status_message.content, "info", self.id, self.name)
|
|
|
|
# Code execution successful
|
|
output = result.get("output", "")
|
|
execution_result = result.get("result")
|
|
logging_utils.info("Code executed successfully", "execution")
|
|
|
|
# Format response content
|
|
response_content = f"## Code executed successfully"
|
|
|
|
# Add correction attempts info if any corrections were made
|
|
if attempts_info and len(attempts_info) > 1:
|
|
response_content += f" (after {len(attempts_info)-1} correction attempts)"
|
|
|
|
response_content += "\n\n"
|
|
|
|
# Include the executed code
|
|
response_content += f"### Final Executed Code\n\n```python\n{attempts_info[-1]['code']}\n```\n\n"
|
|
|
|
# Include the output if available
|
|
if output:
|
|
response_content += f"### Output\n\n```\n{output}\n```\n\n"
|
|
|
|
# Create document with results
|
|
data_document = self._create_document_from_result(execution_result)
|
|
if data_document:
|
|
response["documents"].append(data_document)
|
|
|
|
# Include the execution result if available
|
|
if execution_result:
|
|
result_str = json.dumps(execution_result, indent=2) if isinstance(execution_result, (dict, list)) else str(execution_result)
|
|
response_content += f"### Result\n\n```\n{result_str}\n```\n\n"
|
|
|
|
# Include correction history if any corrections were made
|
|
if attempts_info and len(attempts_info) > 1:
|
|
response_content += f"### Code Correction History\n\n"
|
|
for i, attempt in enumerate(attempts_info[:-1], 1):
|
|
response_content += f"**Attempt {i}:**\n\n"
|
|
response_content += f"```python\n{attempt['code']}\n```\n\n"
|
|
response_content += f"**Error:**\n\n```\n{attempt['error']}\n```\n\n"
|
|
|
|
# Create a correction attempt document for each attempt
|
|
attempt_doc_id = f"correction_{uuid.uuid4()}"
|
|
attempt_filename = f"correction_attempt_{i}.py"
|
|
|
|
attempt_document = {
|
|
"id": attempt_doc_id,
|
|
"source": {
|
|
"type": "generated",
|
|
"id": attempt_doc_id,
|
|
"name": attempt_filename,
|
|
"content_type": "text/x-python",
|
|
"size": len(attempt['code'])
|
|
},
|
|
"contents": [{
|
|
"type": "text",
|
|
"text": attempt['code'],
|
|
"is_extracted": True
|
|
}]
|
|
}
|
|
|
|
# Add correction document to response
|
|
response["documents"].append(attempt_document)
|
|
logging_utils.info(f"Added correction attempt file '{attempt_filename}' to response", "agents")
|
|
|
|
response["content"] = response_content
|
|
|
|
# Process any files created by the code
|
|
if isinstance(execution_result, dict) and "created_files" in execution_result:
|
|
created_files = execution_result.get("created_files", [])
|
|
for file_info in created_files:
|
|
file_id = file_info.get("id")
|
|
if file_id:
|
|
logging_utils.info(f"Adding created file {file_info.get('name', file_id)} to documents", "files")
|
|
# Add file document to the response
|
|
doc = {
|
|
"id": f"doc_{uuid.uuid4()}",
|
|
"source": file_info,
|
|
"type": "file"
|
|
}
|
|
response["documents"].append(doc)
|
|
else:
|
|
# Code execution failed after all attempts
|
|
error = result.get("error", "Unknown error")
|
|
logging_utils.error(f"Error during code execution after all correction attempts: {error}", "execution")
|
|
|
|
# Format error response
|
|
response_content = f"## Error during code execution\n\n"
|
|
|
|
# Include correction attempts information
|
|
if attempts_info:
|
|
response_content += f"I made {len(attempts_info)} attempts to correct the code, but couldn't resolve all issues.\n\n"
|
|
|
|
# Add the final attempt
|
|
response_content += f"### Final Code Attempt\n\n```python\n{attempts_info[-1]['code']}\n```\n\n"
|
|
response_content += f"### Final Error\n\n```\n{attempts_info[-1]['error']}\n```\n\n"
|
|
|
|
# Add recommendation based on error
|
|
response_content += get_error_recommendation(error)
|
|
|
|
# Add correction history
|
|
if len(attempts_info) > 1:
|
|
response_content += f"\n### Code Correction History\n\n"
|
|
for i, attempt in enumerate(attempts_info[:-1], 1):
|
|
response_content += f"**Attempt {i}:**\n\n"
|
|
response_content += f"```python\n{attempt['code']}\n```\n\n"
|
|
response_content += f"**Error:**\n\n```\n{attempt['error']}\n```\n\n"
|
|
|
|
# Create a correction attempt document for each attempt
|
|
attempt_doc_id = f"correction_{uuid.uuid4()}"
|
|
attempt_filename = f"correction_attempt_{i}.py"
|
|
|
|
attempt_document = {
|
|
"id": attempt_doc_id,
|
|
"source": {
|
|
"type": "generated",
|
|
"id": attempt_doc_id,
|
|
"name": attempt_filename,
|
|
"content_type": "text/x-python",
|
|
"size": len(attempt['code'])
|
|
},
|
|
"contents": [{
|
|
"type": "text",
|
|
"text": attempt['code'],
|
|
"is_extracted": True
|
|
}]
|
|
}
|
|
|
|
# Add correction document to response
|
|
response["documents"].append(attempt_document)
|
|
logging_utils.info(f"Added correction attempt file '{attempt_filename}' to response", "agents")
|
|
else:
|
|
# Just show the code and error
|
|
response_content += f"### Executed Code\n\n```python\n{code_to_execute}\n```\n\n"
|
|
response_content += f"### Error\n\n```\n{error}\n```\n\n"
|
|
|
|
# Add recommendation based on error
|
|
response_content += get_error_recommendation(error)
|
|
|
|
response["content"] = response_content
|
|
else:
|
|
# No code to execute
|
|
response["content"] = "I couldn't find or generate executable code. Please provide Python code or explain your requirements more clearly."
|
|
|
|
# Finalize response
|
|
self.message_utils.finalize_message(response)
|
|
|
|
# Log completion - 100% progress
|
|
if log_func:
|
|
status_message = self.protocol.create_status_update_message(
|
|
status_description="Code execution complete",
|
|
sender_id=self.id,
|
|
status="completed",
|
|
progress=1.0,
|
|
context_id=workflow_id
|
|
)
|
|
log_func(workflow_id, status_message.content, "info", self.id, self.name)
|
|
|
|
# Log success
|
|
logging_utils.info("CoderAgent has successfully processed the request", "agents")
|
|
|
|
return response
|
|
|
|
except Exception as e:
|
|
error_msg = f"Error during processing by the CoderAgent: {str(e)}"
|
|
logging_utils.error(error_msg, "error")
|
|
|
|
# Create error response
|
|
response["content"] = f"## Processing Error\n\n```\n{error_msg}\n\n{traceback.format_exc()}\n```"
|
|
self.message_utils.finalize_message(response)
|
|
|
|
# Log error status
|
|
if log_func:
|
|
status_message = self.protocol.create_status_update_message(
|
|
status_description=f"Error during code execution: {str(e)}",
|
|
sender_id=self.id,
|
|
status="error",
|
|
progress=1.0,
|
|
context_id=workflow_id
|
|
)
|
|
log_func(workflow_id, status_message.content, "error", self.id, self.name)
|
|
|
|
return response
|
|
|
|
|
|
|
|
|
|
def _create_document_from_result(self, execution_result, output_format="json"):
|
|
"""
|
|
Create a document object from execution results
|
|
|
|
Args:
|
|
execution_result: The data returned from code execution
|
|
output_format: Desired format (json, csv, etc.)
|
|
|
|
Returns:
|
|
Document object for passing to other agents
|
|
"""
|
|
if not execution_result:
|
|
return None
|
|
|
|
doc_id = f"data_{uuid.uuid4()}"
|
|
|
|
# Determine filename and content type based on the data
|
|
if isinstance(execution_result, pd.DataFrame):
|
|
# Handle DataFrame result
|
|
filename = "processed_data.csv"
|
|
content_type = "text/csv"
|
|
content = execution_result.to_csv(index=False)
|
|
elif isinstance(execution_result, dict) or isinstance(execution_result, list):
|
|
# Handle dictionary or list result
|
|
filename = "processed_data.json"
|
|
content_type = "application/json"
|
|
content = json.dumps(execution_result)
|
|
elif isinstance(execution_result, str):
|
|
# Try to determine if string is JSON, CSV, or plain text
|
|
if execution_result.strip().startswith('{') or execution_result.strip().startswith('['):
|
|
filename = "processed_data.json"
|
|
content_type = "application/json"
|
|
elif ',' in execution_result and '\n' in execution_result:
|
|
filename = "processed_data.csv"
|
|
content_type = "text/csv"
|
|
else:
|
|
filename = "processed_data.txt"
|
|
content_type = "text/plain"
|
|
content = str(execution_result)
|
|
else:
|
|
# Default case for other types
|
|
filename = "processed_data.txt"
|
|
content_type = "text/plain"
|
|
content = str(execution_result)
|
|
|
|
# Create document object
|
|
document = {
|
|
"id": doc_id,
|
|
"source": {
|
|
"type": "generated",
|
|
"id": doc_id,
|
|
"name": filename,
|
|
"content_type": content_type,
|
|
},
|
|
"contents": [{
|
|
"type": "text",
|
|
"text": content,
|
|
"is_extracted": True
|
|
}]
|
|
}
|
|
|
|
return document
|
|
|
|
async def _execute_with_auto_correction(
|
|
self,
|
|
initial_code: str,
|
|
requirements: List[str],
|
|
context: Dict[str, Any],
|
|
original_prompt: str,
|
|
logging_utils: LoggingUtils = None
|
|
) -> Tuple[Dict[str, Any], List[Dict[str, Any]]]:
|
|
"""
|
|
Execute code with automatic error correction and retries.
|
|
|
|
Args:
|
|
initial_code: The initial Python code to execute
|
|
requirements: List of required packages
|
|
context: Additional context for execution
|
|
original_prompt: The original user request/prompt
|
|
logging_utils: Optional logging utility
|
|
|
|
Returns:
|
|
Tuple of (final execution result, list of attempt info dictionaries)
|
|
"""
|
|
# Initialize tracking data
|
|
current_code = initial_code
|
|
current_requirements = requirements.copy() if requirements else []
|
|
attempts_info = []
|
|
|
|
# Execute with correction loop
|
|
for attempt in range(1, self.max_correction_attempts + 1):
|
|
if logging_utils:
|
|
if attempt == 1:
|
|
logging_utils.info(f"Executing code (attempt {attempt}/{self.max_correction_attempts})", "execution")
|
|
else:
|
|
logging_utils.info(f"Executing corrected code (attempt {attempt}/{self.max_correction_attempts})", "execution")
|
|
|
|
# Execute the current code version
|
|
result = await self._execute_code(current_code, current_requirements, context)
|
|
|
|
# Record attempt information
|
|
attempts_info.append({
|
|
"attempt": attempt,
|
|
"code": current_code,
|
|
"error": result.get("error", ""),
|
|
"success": result.get("success", False)
|
|
})
|
|
|
|
# Check if execution was successful
|
|
if result.get("success", False):
|
|
# Success! Return the result and attempt info
|
|
return result, attempts_info
|
|
|
|
# Failed execution - check if we've reached the maximum attempt limit
|
|
if attempt >= self.max_correction_attempts:
|
|
if logging_utils:
|
|
logging_utils.warning(f"Maximum correction attempts ({self.max_correction_attempts}) reached, giving up", "execution")
|
|
break
|
|
|
|
# Need to correct the code - generate a fix based on the error
|
|
error_message = result.get("error", "Unknown error")
|
|
|
|
if logging_utils:
|
|
logging_utils.info(f"Attempting to fix code error: {error_message[:200]}...", "execution")
|
|
|
|
# Generate corrected code
|
|
corrected_code, new_requirements = await self._generate_code_correction(
|
|
current_code,
|
|
error_message,
|
|
original_prompt,
|
|
current_requirements
|
|
)
|
|
|
|
# Update for next attempt
|
|
if corrected_code:
|
|
current_code = corrected_code
|
|
|
|
# Add any new requirements
|
|
if new_requirements:
|
|
for req in new_requirements:
|
|
if req not in current_requirements:
|
|
current_requirements.append(req)
|
|
if logging_utils:
|
|
logging_utils.info(f"Added new requirement: {req}", "execution")
|
|
else:
|
|
# Could not generate correction, break out of the loop
|
|
if logging_utils:
|
|
logging_utils.warning("Could not generate code correction, giving up", "execution")
|
|
break
|
|
|
|
# If we get here, all attempts failed - return the last result and attempt info
|
|
return result, attempts_info
|
|
|
|
async def _generate_code_correction(
|
|
self,
|
|
code: str,
|
|
error_message: str,
|
|
original_prompt: str,
|
|
current_requirements: List[str] = None
|
|
) -> Tuple[str, List[str]]:
|
|
"""
|
|
Generate a corrected version of code based on error messages.
|
|
|
|
Args:
|
|
code: The code that produced errors
|
|
error_message: The error message to fix
|
|
original_prompt: The original task/requirements
|
|
current_requirements: List of currently required packages
|
|
|
|
Returns:
|
|
Tuple of (corrected code, new requirements list)
|
|
"""
|
|
try:
|
|
# Create a detailed prompt for code correction
|
|
correction_prompt = f"""You need to fix an error in Python code. The code was written for this task:
|
|
|
|
ORIGINAL TASK:
|
|
{original_prompt}
|
|
|
|
CURRENT CODE:
|
|
```python
|
|
{code}
|
|
```
|
|
|
|
ERROR MESSAGE:
|
|
```
|
|
{error_message}
|
|
```
|
|
|
|
CURRENT REQUIREMENTS: {', '.join(current_requirements) if current_requirements else "None"}
|
|
|
|
Your task is to analyze the error and provide a corrected version of the code.
|
|
Focus specifically on fixing the error while preserving the original functionality.
|
|
|
|
Common fixes might include:
|
|
- Fixing syntax errors (missing parentheses, indentation, etc.)
|
|
- Resolving import errors by adding appropriate requirements
|
|
- Correcting file paths or handling file not found errors
|
|
- Adding error handling for specific edge cases
|
|
- Fixing logical errors in the code
|
|
|
|
FORMAT INSTRUCTIONS:
|
|
1. Provide ONLY the complete fixed Python code without ANY explanation
|
|
2. DO NOT include code block markers like ```python or ```
|
|
3. DO NOT explain what the code does before or after it
|
|
4. DO NOT include any text that is not valid Python code
|
|
5. Start your response directly with the valid Python code
|
|
6. End your response with valid Python code
|
|
|
|
If you need to add new required packages, place them in a specially formatted comment at the top of your code like this:
|
|
# REQUIREMENTS: package1,package2,package3
|
|
|
|
Your entire response must be valid Python that can be executed without modification.
|
|
"""
|
|
|
|
# Create messages for the API
|
|
messages = [
|
|
{"role": "system", "content": "You are a Python debugging expert. You provide ONLY clean, fixed Python code without any explanations, markdown formatting, or non-code text. Your response should be nothing but valid, fixed Python code that can be executed directly."},
|
|
{"role": "user", "content": correction_prompt}
|
|
]
|
|
|
|
# Call the API with very low temperature for deterministic fixes
|
|
generated_content = await self.ai_service.call_api(
|
|
messages,
|
|
temperature=self.correction_temperature,
|
|
max_tokens=self.ai_max_tokens
|
|
)
|
|
|
|
# Clean the generated content to ensure it's only valid Python code
|
|
fixed_code = self._clean_code(generated_content)
|
|
|
|
# Extract requirements from special comment at the top of the code
|
|
new_requirements = []
|
|
for line in fixed_code.split('\n'):
|
|
if line.strip().startswith("# REQUIREMENTS:"):
|
|
req_str = line.replace("# REQUIREMENTS:", "").strip()
|
|
new_requirements = [r.strip() for r in req_str.split(',') if r.strip()]
|
|
break
|
|
|
|
return fixed_code, new_requirements
|
|
|
|
except Exception as e:
|
|
logging.error(f"Error generating code correction: {str(e)}", exc_info=True)
|
|
# Return None to indicate failure
|
|
return None, []
|
|
|
|
def _clean_code(self, code: str) -> str:
|
|
"""
|
|
Clean up code by removing markdown code block markers and other formatting artifacts.
|
|
|
|
Args:
|
|
code: The code string to clean
|
|
|
|
Returns:
|
|
Cleaned code string
|
|
"""
|
|
import re
|
|
|
|
# Remove code block markers at beginning/end
|
|
code = re.sub(r'^```(?:python)?\s*', '', code)
|
|
code = re.sub(r'```\s*$', '', code)
|
|
|
|
# Remove any trailing markdown code blocks that might have been added by the AI
|
|
lines = code.split('\n')
|
|
clean_lines = []
|
|
|
|
# Flag to track if we're in a trailing markdown section
|
|
in_trailing_markdown = False
|
|
|
|
for line in reversed(lines):
|
|
stripped = line.strip()
|
|
|
|
# Check if this line contains only backticks (``` or ` or ``)
|
|
if re.match(r'^`{1,3}$', stripped):
|
|
in_trailing_markdown = True
|
|
continue
|
|
|
|
# Check if this is a markdown comment or note
|
|
if in_trailing_markdown and (stripped.startswith('#') or
|
|
stripped.lower().startswith('note:') or
|
|
stripped.lower().startswith('example:')):
|
|
continue
|
|
|
|
# If we've reached actual code, stop considering trailing markdown
|
|
if stripped and not in_trailing_markdown:
|
|
in_trailing_markdown = False
|
|
|
|
# Add this line if it's not part of trailing markdown
|
|
if not in_trailing_markdown:
|
|
clean_lines.insert(0, line)
|
|
|
|
# Join the lines back together
|
|
clean_code = '\n'.join(clean_lines)
|
|
|
|
# Final cleanup for any stray backticks
|
|
clean_code = re.sub(r'`{1,3}\s*$', '', clean_code)
|
|
|
|
return clean_code.strip()
|
|
|
|
async def _generate_code_from_prompt(self, prompt: str, documents: List[Dict[str, Any]]) -> Tuple[str, List[str]]:
|
|
"""
|
|
Generate Python code from a prompt using AI service.
|
|
|
|
Args:
|
|
prompt: The prompt to generate code from
|
|
documents: Documents associated with the prompt
|
|
|
|
Returns:
|
|
Tuple of (generated Python code, required packages)
|
|
"""
|
|
try:
|
|
# Prepare a prompt for code generation
|
|
ai_prompt = f"""Generate Python code to solve the following task:
|
|
{prompt}
|
|
|
|
Available input files:
|
|
"""
|
|
# Add information about available documents
|
|
if documents:
|
|
for i, doc in enumerate(documents):
|
|
source = doc.get("source", {})
|
|
doc_name = source.get("name", f"Document {i+1}")
|
|
doc_type = source.get("content_type", "unknown")
|
|
doc_id = source.get("id", "")
|
|
|
|
ai_prompt += f"- {doc_name} (type: {doc_type}, id: {doc_id}, path: './input_data/{doc_name}')\n"
|
|
else:
|
|
ai_prompt += "No input files available.\n"
|
|
|
|
ai_prompt += """
|
|
IMPORTANT REQUIREMENTS:
|
|
1. Your code MUST define a 'result' variable to store the final output of your code.
|
|
2. At the end of your script, it should print or output the result variable.
|
|
3. Make your 'result' variable a dictionary or another JSON-serializable data structure that contains all relevant output.
|
|
4. Input files are accessible in the './input_data/' directory.
|
|
5. Keep code well-documented with comments explaining key operations.
|
|
6. Make your code complete and self-contained.
|
|
7. Include proper error handling.
|
|
|
|
FORMAT INSTRUCTIONS:
|
|
- Provide ONLY the Python code without ANY introduction, explanation, or conclusion text
|
|
- DO NOT include code block markers like ```python or ```
|
|
- DO NOT explain what the code does before or after it
|
|
- DO NOT include any text that is not valid Python code
|
|
- Start your response directly with valid Python code
|
|
- End your response with valid Python code
|
|
|
|
For required packages, place them in a specially formatted comment at the top of your code one one line like this:
|
|
# REQUIREMENTS: pandas,numpy,matplotlib,requests
|
|
|
|
Your entire response must be valid Python that can be executed without modification.
|
|
"""
|
|
|
|
# Create messages for the API
|
|
messages = [
|
|
{"role": "system", "content": "You are a Python code generator that provides ONLY clean, executable Python code without any explanations, markdown formatting, or non-code text. Your response should be nothing but valid Python code that can be executed directly."},
|
|
{"role": "user", "content": ai_prompt}
|
|
]
|
|
|
|
# Call the API
|
|
logging.info(f"Calling AI API to generate code")
|
|
generated_content = await self.ai_service.call_api(messages, temperature=self.ai_temperature, max_tokens=self.ai_max_tokens)
|
|
|
|
# Clean the generated content to ensure it's only valid Python code
|
|
code = self._clean_code(generated_content)
|
|
|
|
# Extract requirements from special comment at the top of the code
|
|
requirements = []
|
|
for line in code.split('\n'):
|
|
if line.strip().startswith("# REQUIREMENTS:"):
|
|
req_str = line.replace("# REQUIREMENTS:", "").strip()
|
|
requirements = [r.strip() for r in req_str.split(',') if r.strip()]
|
|
break
|
|
|
|
return code, requirements
|
|
|
|
except Exception as e:
|
|
logging.error(f"Error generating code with AI: {str(e)}", exc_info=True)
|
|
# Return basic error handling code and no requirements
|
|
error_str = str(e).replace('"', '\\"')
|
|
return f"""
|
|
# Error during code generation
|
|
print(f"An error occurred during code generation: {error_str}")
|
|
# Return an error result
|
|
result = {{"error": "Code generation failed", "message": "{error_str}"}}
|
|
""", []
|
|
|
|
async def _execute_code(self, code: str, requirements: List[str] = None, context: Dict[str, Any] = None) -> Dict[str, Any]:
|
|
"""
|
|
Execute Python code using the SimpleCodeExecutor.
|
|
|
|
Args:
|
|
code: The Python code to execute
|
|
requirements: List of required packages
|
|
context: Additional context for execution
|
|
|
|
Returns:
|
|
Result of code execution
|
|
"""
|
|
# Get workflow ID and set up logging
|
|
workflow_id = context.get("workflow_id", "") if context else ""
|
|
logging_utils = None
|
|
if "log_func" in context and workflow_id:
|
|
logging_utils = LoggingUtils(workflow_id, context.get("log_func"))
|
|
|
|
if logging_utils:
|
|
logging_utils.info("Executing Python code", "execution")
|
|
if requirements:
|
|
logging_utils.info(f"Required packages: {', '.join(requirements)}", "execution")
|
|
|
|
try:
|
|
# List of blocked packages for security
|
|
blocked_packages = [
|
|
"cryptography", "flask", "django", "tornado", # Security risks
|
|
"tensorflow", "pytorch", "scikit-learn" # Resource intensive
|
|
]
|
|
|
|
# Initialize SimpleCodeExecutor with requirements and workflow_id for persistence
|
|
executor = SimpleCodeExecutor(
|
|
workflow_id=workflow_id,
|
|
timeout=self.executor_timeout,
|
|
max_memory_mb=self.executor_memory_limit,
|
|
requirements=requirements,
|
|
blocked_packages=blocked_packages,
|
|
ai_service = self.ai_service
|
|
)
|
|
|
|
# Prepare input data for the code
|
|
input_data = {"context": context, "workflow_id": workflow_id}
|
|
|
|
# Add file references if available
|
|
if context and "documents" in context:
|
|
input_data["files"] = [
|
|
{
|
|
"id": doc.get("source", {}).get("id", ""),
|
|
"name": doc.get("source", {}).get("name", ""),
|
|
"type": doc.get("source", {}).get("content_type", ""),
|
|
"path": doc.get("source", {}).get("path", "") # Full file path
|
|
}
|
|
for doc in context.get("documents", [])
|
|
if doc.get("source", {}).get("type") == "file"
|
|
]
|
|
|
|
# Extract document content from message but don't create files yet
|
|
if context and "message" in context and "content" in context["message"]:
|
|
message_content = context["message"]["content"]
|
|
|
|
# Check if there's extracted document content
|
|
if "=== EXTRACTED DOCUMENT CONTENT ===" in message_content:
|
|
# Add a special field to input_data for extracted content
|
|
input_data["extracted_documents"] = []
|
|
|
|
# Split by the document marker pattern
|
|
pattern = r"--- (.*?) ---\s*"
|
|
import re
|
|
doc_sections = re.split(pattern, message_content)
|
|
|
|
# Skip the first section (before any "--- doc ---" marker)
|
|
for i in range(1, len(doc_sections), 2):
|
|
if i+1 < len(doc_sections):
|
|
doc_name = doc_sections[i].strip()
|
|
doc_content = doc_sections[i+1].strip()
|
|
|
|
# Store the extracted content to be processed by the executor
|
|
input_data["extracted_documents"].append({
|
|
"name": doc_name,
|
|
"content": doc_content,
|
|
"type": "text/csv" if doc_name.endswith(".csv") else "text/plain"
|
|
})
|
|
if logging_utils:
|
|
logging_utils.info(f"Extracted document content: {doc_name}", "execution")
|
|
|
|
|
|
# Execute the code
|
|
if logging_utils:
|
|
logging_utils.info(f"Executing code with input data containing {len(input_data.get('files', []))} files", "execution")
|
|
|
|
result = executor.execute_code(code, input_data)
|
|
|
|
# Log the execution results
|
|
if logging_utils:
|
|
if result.get("success", False):
|
|
logging_utils.info("Code executed successfully", "execution")
|
|
|
|
# Log a preview of the output
|
|
output = result.get("output", "")
|
|
if output:
|
|
preview = output[:1000] + "..." if len(output) > 1000 else output
|
|
logging_utils.info(f"Output preview: {preview}", "execution")
|
|
|
|
# Log a preview of the result
|
|
execution_result = result.get("result")
|
|
if execution_result:
|
|
if isinstance(execution_result, (dict, list)):
|
|
result_str = json.dumps(execution_result, indent=2)
|
|
preview = result_str[:1000] + "..." if len(result_str) > 1000 else result_str
|
|
else:
|
|
str_result = str(execution_result)
|
|
preview = str_result[:1000] + "..." if len(str_result) > 1000 else str_result
|
|
|
|
logging_utils.info(f"Result preview: {preview}", "execution")
|
|
else:
|
|
# Log error information
|
|
error = result.get("error", "Unknown error")
|
|
logging_utils.error(f"Error during code execution: {error}", "execution")
|
|
|
|
# Clean up non-persistent environments
|
|
if not executor.is_persistent:
|
|
executor.cleanup()
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
error_message = f"Error during code execution: {str(e)}\n{traceback.format_exc()}"
|
|
|
|
if logging_utils:
|
|
logging_utils.error(error_message, "error")
|
|
|
|
return {
|
|
"success": False,
|
|
"output": "",
|
|
"error": error_message,
|
|
"result": None
|
|
}
|
|
|
|
|
|
def send_error_message(self, error_description: str, sender_id: str, receiver_id: str = None, context_id: str = None) -> AgentMessage:
|
|
"""Send an error message using the protocol"""
|
|
return self.protocol.create_error_message(
|
|
error_description=error_description,
|
|
sender_id=sender_id,
|
|
receiver_id=receiver_id,
|
|
error_type="code_execution",
|
|
context_id=context_id
|
|
)
|
|
|
|
def send_result_message(self, result_content: str, sender_id: str, receiver_id: str, task_id: str,
|
|
output_data: Dict[str, Any] = None, context_id: str = None) -> AgentMessage:
|
|
"""Send a result message using the protocol"""
|
|
return self.protocol.create_result_message(
|
|
result_content=result_content,
|
|
sender_id=sender_id,
|
|
receiver_id=receiver_id,
|
|
task_id=task_id,
|
|
output_data=output_data,
|
|
result_format="python_code",
|
|
context_id=context_id
|
|
)
|
|
|
|
# Singleton instance
|
|
_coder_agent = None
|
|
|
|
def get_coder_agent():
|
|
"""Returns a singleton instance of the Coder Agent"""
|
|
global _coder_agent
|
|
if _coder_agent is None:
|
|
_coder_agent = CoderAgent()
|
|
return _coder_agent |